Changes in the Characteristics and Levels of Comorbidity Among New Patients Into Methadone Maintenance Treatment Program in British Columbia During Its Expansion Period From 1998–2006
Bibliographic record
Abstract
We described the changing characteristics and comorbidity levels of new patients into Methadone maintenance treatment (MMT) program in British Columbia, Canada, during its expansion period of 1998-2006. Analyses used administrative data. Generalized regression models were applied using Charlson Comorbidity Index (CCI) and Chronic Disease Score (CDS) as outcomes. 12,615 individuals initiated MMT during 1998-2006, while their odds of having moderate CCI (1 ≤ CCI ≤ 4) and mean CDS increased by 60% and 11%, respectively, after adjusting for confounders. MMT entrants were presented with progressively higher levels of comorbidity, independent of other characteristics. Future MMT policies should address higher levels of comorbidity among new patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".